Configure the search parameters here:

if (!snapshot_loaded) {
  # query start date/time (inclusive)
  rangestart <- "2022-04-01 00:00:00"
  
  # query end date/time (exclusive)
  rangeend <- "2022-06-01 00:00:00"
  
  # text filter restricts results to only those containing words, phrases, or meeting a boolean condition. This query syntax is very flexible and supports a wide variety of filter scenarios:
  # words: text_filter <- "cdc nih who"  ...contains "cdc" or "nih" or "who"
  # phrase: text_filter <- '"vitamin c"' ...contains exact phrase "vitamin c"
  # boolean condition: <- '(cdc nih who) +"vitamin c"' ...contains ("cdc" or "nih" or "who") and exact phrase "vitamin c"
  #full specification here: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-simple-query-string-query.html
  text_filter <- ""
  
  # location filter acts like text filter except applied to the location of the tweet instead of its text body.
  location_filter <- ""
  
  # if FALSE, location filter considers both user-povided and geotagged locations. If TRUE, only geotagged locations are considered.
  must_have_geo <- FALSE
  
  # query semantic similarity phrase (choose one of these examples or enter your own)
  #semantic_phrase <- "Elementary school students are not coping well with distance learning."
  #semantic_phrase <- "I am diabetic and out of work because of coronavirus. I am worried I won't be able to get insulin without insurance."
  semantic_phrase <- ""
  
  # sentiment type (only 'vader' and 'roberta' are supported for now)
  # if the requested sentiment type is not available for the current index or sample, the sentiment
  # column in the result set will contain NA values.
  sentiment_type <- "roberta"
  
  # query lower bound for sentiment (inclusive). Enter a numeric value or for no lower bound set to NA.
  sentiment_lower <- NA
  
  # query upper bound for sentiment (inclusive). Enter a numeric value or for no upper bound set to NA.
  sentiment_upper <- NA
  
  # embedding type (only 'use_large' and 'sbert' are supported for now)
  embedding_type <- "sbert"
  
  # return results in chronological order or as a random sample within the range
  # (ignored if semantic_phrase is not blank)
  random_sample <- TRUE
  
  # number of results to return (to return all results, set to NA)
  resultsize <- 10000
  
  # minimum number of results to return. This should be set according to the needs of the analysis (i.e. enough samples for statistical significance)
  min_results <- 500
}

Configure the hyperparameters here:

if (!snapshot_loaded) {
  #--------------------------
  # PSEUDORANDOM SEEDS
  #--------------------------
  # Optionally specify seeds for reproducibility. For no seed, set to NA on any of these settings.
  # seed for random sampling (if enabled)
  random_seed <- 100
  # seed for k-means
  kmeans_clusters_seed <- 300
  kmeans_subclusters_seed <- 500
  # seed for t-sne
  tsne_clusters_seed <- 700
  tsne_subclusters_seed <- 900
  
  #--------------------------
  # K-MEANS HYPERPARAMS
  #--------------------------
  # range of k choices to test for elbow and silhouette plots
  k_test_range <- 2:40
  
  # number of random starts
  kmeans_nstart <- 25
  
  # maximum iterations
  kmeans_max_iter <- 200
  
  # number of high level clusters
  k <- 9
  
  # number of subclusters per high level cluster
  cluster.k <- 9
  
  #--------------------------
  # LABELING HYPERPARAMS
  #--------------------------
  # Construct master label using top-k words across the entire sample
  master_label_top_k <- 8
  
  # Construct cluster labels using top-k words across each cluster
  cluster_label_top_k <- 3
  
  # Construct subcluster labels using top-k words across each subcluster
  subcluster_label_top_k <- 3
  
  #--------------------------
  # SUMMARIZATION HYPERPARAMS
  #--------------------------
  # number of nearest neighbors to the center to use for subcluster summarization
  summarize_center_nn <- 20
  
  # summarization model inference hyperparameters
  summarize_model <- "sshleifer/distilbart-xsum-12-6"
  summarize_max_len <- 60
  summarize_num_beams <- 6
  summarize_temperature <- 1.0
  
  #--------------------------
  # T-SNE HYPERPARAMS
  #--------------------------
  # hyperparams for clusters
  tsne_clusters_perplexity <- 25
  tsne_clusters_max_iter <- 1500
  
  # hyperparams for subclusters
  tsne_subclusters_perplexity <- 12
  tsne_subclusters_max_iter <- 750
}

Configure the output settings here:

# plot mode - '2d' or '3d'.
# Note: if loading a snapshot, changing the plot mode will trigger T-SNE to re-run.
plot_mode <- "2d"

# If TRUE, cluster and subcluster summaries will be generated using nearest neighbors to the center.
# Note: if loading a snapshot, disabling summarization will do nothing if it was previously enabled.
summarize_clusters <- TRUE
# True to show a listing of all cluster & subcluster summaries
show_summaries_table <- TRUE

# If TRUE, run and output k-means elbow plots
# Note: if loading a snapshot, disabling elbow plots will do nothing if they were previously enabled.
compute_elbow <- FALSE
# If TRUE, run and output k-means silhouette plots
# Note: if loading a snapshot, disabling silhouette plots will do nothing if they were previously enabled.
compute_silhouette <- FALSE

# show/hide extra info (temporary until tabs are implemented)
show_original_subcluster_plots <- FALSE
show_regrouped_subcluster_plots <- TRUE
show_word_scores <- FALSE
show_center_nn <- FALSE

# visualize sentiment and divisiveness
show_overall_sentiment_discrete <- TRUE
show_overall_sentiment_map <- FALSE
show_overall_sentiment_continuous <- FALSE
show_cluster_sentiment_continuous <- FALSE
show_cluster_sentiment_discrete <- TRUE
show_cluster_divisiveness <- FALSE
# threshold that represents the cutoff from neutral to positive above zero
# and neutral to negative below zero. Used to turn sentiment into a variable with discrete values
sentiment_threshold <- 0.25 # 0.05 recommended for VADER sentiment
# sentiment plots start date (set NA to infer from search results)
plot_range_start <- NA
# sentiment plots end date (set NA to infer from search results)
plot_range_end <- NA
# perform linear regression analysis on weekly divisiveness
perform_divisiveness_linreg <- FALSE
## Warning: Ignoring unknown parameters: width

 

Displaying 10000 of 984480 results:

 

Cluster & Subcluster Summary Listing
Cluster Label Generated Summary
Cluster 1. nazi / nazis / germany Social media users have been sharing their reaction to the news that Nazi Germany has admitted its role in World War Two.
………1.1. azov / zelensky / battalion People on social media have been reacting to President Viktor Zelensky’s support for the Nazi Azov battalion.
………1.2. flag / symbol / neonazi Following the fatal shooting of a man in the US state of Buffalo, people have been sharing their reaction on social media.
………1.3. fascism / society / culture People on social media have been reacting to the news that Ukraine’s President Vladimir Putin has declared the country a “fascist”.
………1.4. elonmusk / nazi’s / scum People on Twitter have been reacting to a tweet by a Democratic activist who said he was a Nazi.
………1.5. german / 11 / dog Social media users have been reacting to the news that a Jewish man has been accused of being a Nazi.
………1.6. left / racist / carlson People on Twitter have been debating whether there is a right-wing “neo-Nazi” in the US.
………1.7. israel / jews / palestinian Following the killing of a Palestinian journalist by the Israeli Defence Forces (IDF), social media users have been reacting with a mixture of anger and sympathy.
………1.8. wwii / wwiii / ww3 People have been reacting to the news that Nazi Germany has admitted its role in World War Two.
………1.9. russians / mfarussia / real People on social media have been reacting to President Vladimir Putin’s comments that Ukraine is “nazis”.
Cluster 2. media / twitter / lies Twitter users have been sharing their reaction to the news that Elon Musk is buying Twitter.
………2.1. biden / president / biden’s People on Twitter have been reacting to the news that former Vice-President Joe Biden is “brain dead” and a “puppet for the cabal”.
………2.2. oligarch / elon / musk Twitter users have been reacting to the news that billionaire Elon Musk is buying the social media site.
………2.3. free / elonmusk / fear People on Twitter have been reacting to a report on the Clinton email server by saying it was “propaganda”.
………2.4. refugees / asylum / seekers People have been reacting to Boris Johnson’s call for the UK to take in refugees.
………2.5. india / imran / indians People in Pakistan have been reacting on social media to Mehdi Hasan’s comments on India.
………2.6. people / care / gun People have been reacting to Justin Trudeau’s open letter to the World Economic Forum (WEF) calling on him to stop promoting “propaganda”.
………2.7. invasion / terrorist / israel People on social media have been reacting to the news that Ethiopia is using rape as a weapon of war in Tigray.
………2.8. news / cnn / truth Social media users have been reacting to a report by the AFP news agency that said Russian state media was responsible for the election of Donald Trump as president.
………2.9. democrats / vote / party People have been reacting on social media to Joe Biden’s announcement that he will not be running for re-election.
Cluster 3. ukrainians / weapons / russia’s Following reports that more than 1,000 Russian soldiers have been killed in eastern Ukraine, BBC News looks at the reaction on social media to the latest developments in the conflict.
………3.1. ukrainerussiawar / standwithukraine / stoprussia Social media users have been reacting to the news that Ukrainian forces have killed more than 1,000 Russian soldiers.
………3.2. media / biden / trump People on Twitter have been reacting to the BBC’s report on why Russians support the war in Ukraine.
………3.3. military / arms / nuclear People have been reacting to President Donald Trump’s announcement that the US is to lend weapons to Ukraine.
………3.4. russiaukraine / day / news People on social media have been reacting to the latest developments in the Russia-Ukraine war.
………3.5. crimea / territory / land People on social media have been reacting to US Vice-President Joe Biden’s announcement that Russia will take control of Ukraine.
………3.6. invasion / china / rus People have been reacting to the Russian invasion of Ukraine on social media, with many saying Russia is the aggressor.
………3.7. win / peace / fight People on social media have been sharing their views on the conflict in eastern Ukraine, with many saying the US and Russia should consider a ceasefire.
………3.8. people / nazi / human People on social media have been debating whether Ukraine is engaging in a genocide.
………3.9. putin’s / oil / vladimir People on social media have been reacting to reports that former Russian generals are preparing to oust President Vladimir Putin to end the war in Ukraine.
Cluster 4. putin’s / trump / vladimir Social media users have been reacting to the news that Russian President Vladimir Putin has undergone cancer surgery and the European Union has imposed sanctions on Russia.
………4.1. putinwarcriminal / elonmusk / homeless People on Twitter have been reacting to a tweet by Boris Johnson suggesting Russia’s President Vladimir Putin is involved in war crimes.
………4.2. hate / gop / party People have been reacting to President Donald Trump’s statement that Russia’s President Vladimir Putin should be punished.
………4.3. china / west / russia’s Social media users have been reacting to the Pope’s suggestion that the Russian invasion of Ukraine may have been started by the West.
………4.4. sanctions / people / banned People on social media have been reacting to the European Union’s decision to impose sanctions on Russia.
………4.5. oil / energy / prices People on Twitter have been reacting to President Donald Trump’s decision to increase gas prices in Ukraine.
………4.6. nuclear / die / nukes People on social media have been reacting to US Vice-President Joe Biden’s comments on Russia’s invasion of Ukraine.
………4.7. standwithukraine / ukrainerussiawar / russianwarcrimes Social media users have been reacting to the news that Russian President Vladimir Putin has been accused of war crimes in Ukraine.
………4.8. welly / cancer / hitler Social media users have been reacting to a report that Russian President Vladimir Putin has undergone cancer surgery.
………4.9. media / lies / disinformation Social media users have been reacting to a video posted by the Russian Foreign Minister Sergei Lavrov on Twitter.
Cluster 5. money / sanctions / billion Following the US House of Representatives vote to give Ukraine $33 billion (£30bn) in aid, here is a round-up of reaction from social media users.
………5.1. billions / send / 4 People have been reacting to President Donald Trump’s announcement that the US is sending a further $40 billion to Ukraine.
………5.2. prices / gas / price US Vice-President Joe Biden has been accused of misleading people by blaming Russia’s war in Ukraine for the recent rise in gas prices.
………5.3. oil / eu / imposed People have been reacting on social media to US and EU sanctions against Russia.
………5.4. chinese / government / countries People on Twitter have been reacting to the US government’s decision to spend $40bn (£30bn) on Ukraine.
………5.5. voted / vote / bill People on Twitter have been reacting to the House of Representatives vote to give Ukraine $33 billion in aid.
………5.6. grain / eu / food Social media users have been reacting to UK Foreign Secretary Boris Johnson’s call for Russia to open Ukrainian ports for grain exports.
………5.7. refugees / people / children People on Twitter have been reacting to the news that more than 10,000 children have gone missing in Ukraine.
………5.8. laundering / oligarchs / eth US President Barack Obama’s decision to give $40bn (£30bn) of US military equipment to Ukraine has been widely criticised on social media.
………5.9. biden / joe / hunter US Vice-President Joe Biden’s request for $33 billion in aid to Ukraine has sparked a flurry of reaction on social media.
Cluster 6. mariupol / azov / azovstal Social media users have been reacting to reports that Russia is losing ground in its war with Ukraine.
………6.1. soviet / union / russia’s Social media users have been reacting to a video showing a Russian military victory in the Ukrainian city of Mariupol.
………6.2. zelensky / zelensky’s / zelenskyy People on social media have been reacting to a report by Ukrainian President Viktor Zelensky that Russia is trying to destabilise Moldova.
………6.3. city / plant / mariupol’s Social media users have been reacting to reports that Russia has captured the eastern Ukrainian city of Mariupol.
………6.4. aid / rescue / refugees Social media users have been reacting to reports of Ukrainian soldiers being evacuated from a Russian-held steelworks.
………6.5. crimes / killed / soldiers People have been reacting to a BBC report on alleged war crimes committed by Russian soldiers in Ukraine.
………6.6. forces / destroyed / strike People on social media have been reacting to reports of Russian military attacks in eastern Ukraine.
………6.7. invasion / soviet / front People have been reacting to reports that Russia is losing ground in its war with Ukraine.
………6.8. savemariupol / defenders / save Social media users have been reacting to the bombing of the Azov Steel Plant in the Ukrainian city of Mariupol.
………6.9. tanks / drones / weapons Twitter users have been reacting to reports that Ukraine is using drone technology in its fight against Russia.
Cluster 7. finland / sweden / turkey Social media users have been sharing their reaction to news that Turkey and Sweden are seeking membership of the Nato defence alliance.
………7.1. forces / 5 / north People on social media have been reacting to a report by the European Union (EU) and US Secretary of State, Joe Biden, that the UK would be able to deploy troops to Poland if it was attacked on US soil.
………7.2. borders / allies / missiles People on social media have been reacting to the news that Russian President Vladimir Putin has called for an end to the Nato alliance.
………7.3. join / membership / apply Social media users have been reacting to Finland’s decision to apply to join the European Union.
………7.4. kyivindependent / action / longer People on social media have been reacting to the news that Ukraine is not a member of the Nato defence alliance.
………7.5. taiwan / china / japan People have been reacting to US President Donald Trump’s announcement that he will hold talks with Taiwan’s leader over a possible Chinese invasion.
………7.6. nato’s / country / people People have been sharing their opinions on whether or not the US should join the European Union’s alliance, NATO.
………7.7. borisjohnson / belarusmfa / spokespersonchn Finland’s decision to join Nato has been met with a mixture of praise and criticism on social media.
………7.8. erdogan / turkish / sweden’s Following Turkish President Recep Tayyip Erdogan’s announcement that Finland and Sweden are seeking membership of Nato, social media users have been reacting to the news.
………7.9. europe / hungary / eu People on social media have been reacting to the news that Switzerland is set to join Nato.
Cluster 8. invasion / promote / refugees A round-up of reaction to the news on social media.
………8.1. zelensky / zelensky’s / zelenskyy People on social media have been reacting to Stephen King’s interview with Mark Zelensky.
………8.2. azov / elonmusk / account Twitter users have been reacting to the announcement by US Senator Rand Paul that his wife will not be travelling with him to Afghanistan.
………8.3. mariupol / children / people People have been reacting to the news that thousands of refugees from war-torn Syria and Iraq are seeking refuge in neighbouring Uganda.
………8.4. music / standwithukraine / spotify Social media users have been reacting to the news of Ukraine-related events on Friday.
………8.5. good / true / truth Here’s a round-up of reactions to the BBC News Channel’s latest programme:
………8.6. pitch / everton / liverpool Football fans have been reacting to Everton’s pitch invasion against Manchester City.
………8.7. assemblageofnft / globalnftcentre / feature Here is a full list of links to the BBC Radio 4 show, which was broadcast on Tuesday.
………8.8. china / years / chernobyl People on social media have been reacting to reports of a Chinese invasion of Taiwan.
………8.9. bad / people / man Social media users have been reacting to the news that US President Donald Trump’s daughter, Angel, has been accused of being a Russian spy.
Cluster 9. eurovision / flag / support BBC News looks at what people have been sharing on social media about the crisis in Ukraine.
………9.1. family / refugees / donate People have been sharing their stories about the crisis in Ukraine on social media.
………9.2. standwithukraine / glory / ukrainerussiawar U2 frontman Bono and his band Edge have been praised on social media for their support for Ukraine in the war with Russia.
………9.3. kyiv / youtube / zelensky Ukrainians have been sharing their views on the crisis in their country on Twitter.
………9.4. kyivindependent / kiev / rusembusa Social media users have been sharing their support for Ukraine on Twitter.
………9.5. song / win / final People have been reacting to Ukraine winning the Eurovision Song Contest 2022.
………9.6. nft / nfts / nftcommunity People have been sharing their pictures and videos on social media as they try to raise money for Ukraine.
………9.7. win / points / voted People from across the world have been reacting to the news that Ukraine is set to win the EU referendum.
………9.8. flags / profile / bio Twitter users have been sharing their reactions to the Ukraine flag on their accounts.
………9.9. ukrainians / government / nazi People in Ukraine have been reacting to a tweet by US President Donald Trump in which he called Ukraine “a fascist invader that only wants to colonise it to exploit its resources”.
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